论文标题
CT图像中的肾脏和肾脏肿瘤分割
Kidney and Kidney Tumour Segmentation in CT Images
论文作者
论文摘要
在计算机断层扫描（CT）图像中自动分割肾脏和肾脏肿瘤是必不可少的，因为与当前的手动分割相比，它使用的时间较少。但是，许多医院仍然依赖医生的手动研究和对CT图像的细分，因为它的准确性更高。因此，本研究的重点是在对比增强的CT图像中开发自动肾脏和肾脏肿瘤分割方法。提出了一种基于卷积神经网络（CNN）的方法，其中开发了3D U-NET分割模型，并训练了从CT扫描中描绘肾脏和肾脏肿瘤。在输入到CNN之前，对每个CT图像进行了预处理，并分析了下采样和贴片输入图像对模型性能的影响。对所提出的方法进行了对公开可用的2021肾脏和肾脏肿瘤分割挑战（Kits21）数据集的评估。具有最佳性能模型的方法的平均训练骰子得分为0.6129，肾脏和肾脏肿瘤骰子得分分别为0.7923和0.4344。对于测试，该模型获得了0.8034的肾脏骰子得分，肾脏肿瘤骰子得分为0.4713，平均骰子得分为0.6374。
Automatic segmentation of kidney and kidney tumour in Computed Tomography (CT) images is essential, as it uses less time as compared to the current gold standard of manual segmentation. However, many hospitals are still reliant on manual study and segmentation of CT images by medical practitioners because of its higher accuracy. Thus, this study focuses on the development of an approach for automatic kidney and kidney tumour segmentation in contrast-enhanced CT images. A method based on Convolutional Neural Network (CNN) was proposed, where a 3D U-Net segmentation model was developed and trained to delineate the kidney and kidney tumour from CT scans. Each CT image was pre-processed before inputting to the CNN, and the effect of down-sampled and patch-wise input images on the model performance was analysed. The proposed method was evaluated on the publicly available 2021 Kidney and Kidney Tumour Segmentation Challenge (KiTS21) dataset. The method with the best performing model recorded an average training Dice score of 0.6129, with the kidney and kidney tumour Dice scores of 0.7923 and 0.4344, respectively. For testing, the model obtained a kidney Dice score of 0.8034, and a kidney tumour Dice score of 0.4713, with an average Dice score of 0.6374.
